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Anomaly Classification
Anomaly Classification On Goodsad
Anomaly Classification On Goodsad
Metrics
AUPR
AUROC
Results
Performance results of various models on this benchmark
Columns
Model Name
AUPR
AUROC
Paper Title
Repository
PatchCore-100%
86.1
85.5
Towards Total Recall in Industrial Anomaly Detection
PatchCore-1%
83.3
81.4
Towards Total Recall in Industrial Anomaly Detection
SimpleNet
78.7
75.3
SimpleNet: A Simple Network for Image Anomaly Detection and Localization
RD4AD
68.2
66.5
Anomaly Detection via Reverse Distillation from One-Class Embedding
DRAEM
71
65.9
DRAEM -- A discriminatively trained reconstruction embedding for surface anomaly detection
SPADE
68.7
64.1
Sub-Image Anomaly Detection with Deep Pyramid Correspondences
NSA
71.8
67.3
Natural Synthetic Anomalies for Self-Supervised Anomaly Detection and Localization
CFLOW-AD
75.3
71.2
CFLOW-AD: Real-Time Unsupervised Anomaly Detection with Localization via Conditional Normalizing Flows
MiniMaxAD-fr
-
86.1
MiniMaxAD: A Lightweight Autoencoder for Feature-Rich Anomaly Detection
-
CutPaste
62.8
60.2
CutPaste: Self-Supervised Learning for Anomaly Detection and Localization
f-AnoGAN
66.6
62.8
f-AnoGAN: Fast Unsupervised Anomaly Detection with Generative Adversarial Networks
0 of 11 row(s) selected.
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